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Animal Sound Recognition Based on Double Feature of Spectrogram

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Due to existence of different environments and noises, the existing method is difficult to ensure the recognition accuracy of animal sound in low Signal-to-noise (SNR) conditions. To address these problems, we propose a double feature, which consists of projection feature and Local binary pattern variance (LBPV) feature, combined with Random forest (RF) for animal sound recognition. In feature extraction, an operation of projecting is made on spectrogram to generate the projection feature. Meanwhile, LBPV feature is generated by means of accumulating the corresponding variances of all pixels for every Uniform local binary pattern (ULBP) in the spectrogram. Short-time spectral estimation algorithm is used to enhance sound signals in severe mismatched noise conditions. In the experiments, we classify 40 kinds of common animal sounds under different SNRs with rain noise, traffic noise, and wind noise. As the experimental results show, the proposed framework consisting of short-time spectrum estimation, double feature, and RF, can recognize a wide range of animal sounds and still remains a recognition rate over 80％ even under 0dB SNR.

Animal sound recognitionLocal binary pattern variance (LBPV)Projection featureRandom forest (RF)Sound enhancement

LI Ying、HUANG Hongkeng、WU Zhibin

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College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116, China

Key Laboratory of Information Security of Network Systems (Fuzhou University), Fuzhou 350116, China

This work is supported by the Natural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China

2018J0179361075022

2019

10.1049/cje.2019.04.005

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(4)
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